Overview

Dataset statistics

Number of variables20
Number of observations7146
Missing cells8168
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 MiB
Average record size in memory491.1 B

Variable types

Numeric13
Categorical3
Text3
DateTime1

Alerts

PropType is highly imbalanced (62.8%)Imbalance
Hbath is highly imbalanced (51.5%)Imbalance
CondoProject has 6261 (87.6%) missing valuesMissing
Extwall has 926 (13.0%) missing valuesMissing
Rooms has 443 (6.2%) missing valuesMissing
Bdrms has 443 (6.2%) missing valuesMissing
Units is highly skewed (γ1 = 42.80622276)Skewed
Lotsize is highly skewed (γ1 = 33.88617801)Skewed
Rooms has 122 (1.7%) zerosZeros
Fbath has 509 (7.1%) zerosZeros
Lotsize has 489 (6.8%) zerosZeros

Reproduction

Analysis started2024-02-28 20:26:13.347981
Analysis finished2024-02-28 20:26:28.923777
Duration15.58 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

PropertyID
Real number (ℝ)

Distinct7055
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178756.77
Minimum98461
Maximum266040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:29.015074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98461
5-th percentile105790.25
Q1136233.25
median176670.5
Q3221564.25
95-th percentile253672.25
Maximum266040
Range167579
Interquartile range (IQR)85331

Descriptive statistics

Standard deviation47982.982
Coefficient of variation (CV)0.26842609
Kurtosis-1.2318058
Mean178756.77
Median Absolute Deviation (MAD)42517
Skewness0.059076039
Sum1.2773959 × 109
Variance2.3023665 × 109
MonotonicityNot monotonic
2024-02-28T21:26:29.132532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176566 2
 
< 0.1%
183128 2
 
< 0.1%
236196 2
 
< 0.1%
214075 2
 
< 0.1%
172832 2
 
< 0.1%
141917 2
 
< 0.1%
114543 2
 
< 0.1%
141962 2
 
< 0.1%
213578 2
 
< 0.1%
213568 2
 
< 0.1%
Other values (7045) 7126
99.7%
ValueCountFrequency (%)
98461 1
< 0.1%
98464 1
< 0.1%
98508 1
< 0.1%
98519 1
< 0.1%
98561 1
< 0.1%
98593 1
< 0.1%
98604 1
< 0.1%
98608 1
< 0.1%
98696 1
< 0.1%
98715 1
< 0.1%
ValueCountFrequency (%)
266040 1
< 0.1%
266025 1
< 0.1%
266017 1
< 0.1%
266009 1
< 0.1%
265996 1
< 0.1%
265962 1
< 0.1%
265958 1
< 0.1%
265953 1
< 0.1%
265945 1
< 0.1%
265842 1
< 0.1%

PropType
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size474.7 KiB
Residential
5774 
Condominium
887 
Commercial
 
240
Lg Apartment
 
238
Manufacturing
 
6

Length

Max length13
Median length11
Mean length11.0007
Min length6

Characters and Unicode

Total characters78611
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowManufacturing
2nd rowCommercial
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Residential 5774
80.8%
Condominium 887
 
12.4%
Commercial 240
 
3.4%
Lg Apartment 238
 
3.3%
Manufacturing 6
 
0.1%
Exempt 1
 
< 0.1%

Length

2024-02-28T21:26:29.248744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T21:26:29.348399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
residential 5774
78.2%
condominium 887
 
12.0%
commercial 240
 
3.3%
lg 238
 
3.2%
apartment 238
 
3.2%
manufacturing 6
 
0.1%
exempt 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 13568
17.3%
e 12027
15.3%
n 7798
9.9%
d 6661
8.5%
a 6264
8.0%
t 6257
8.0%
l 6014
7.7%
R 5774
7.3%
s 5774
7.3%
m 2493
 
3.2%
Other values (14) 5981
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70989
90.3%
Uppercase Letter 7384
 
9.4%
Space Separator 238
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 13568
19.1%
e 12027
16.9%
n 7798
11.0%
d 6661
9.4%
a 6264
8.8%
t 6257
8.8%
l 6014
8.5%
s 5774
8.1%
m 2493
 
3.5%
o 2014
 
2.8%
Other values (7) 2119
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
R 5774
78.2%
C 1127
 
15.3%
L 238
 
3.2%
A 238
 
3.2%
M 6
 
0.1%
E 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78373
99.7%
Common 238
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 13568
17.3%
e 12027
15.3%
n 7798
9.9%
d 6661
8.5%
a 6264
8.0%
t 6257
8.0%
l 6014
7.7%
R 5774
7.4%
s 5774
7.4%
m 2493
 
3.2%
Other values (13) 5743
7.3%
Common
ValueCountFrequency (%)
238
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 13568
17.3%
e 12027
15.3%
n 7798
9.9%
d 6661
8.5%
a 6264
8.0%
t 6257
8.0%
l 6014
7.7%
R 5774
7.3%
s 5774
7.3%
m 2493
 
3.2%
Other values (14) 5981
7.6%

taxkey
Real number (ℝ)

Distinct7055
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4687434 × 109
Minimum30131000
Maximum7.160375 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:29.481623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum30131000
5-th percentile1.1701463 × 109
Q12.3110052 × 109
median3.211215 × 109
Q34.703136 × 109
95-th percentile5.8008152 × 109
Maximum7.160375 × 109
Range7.130244 × 109
Interquartile range (IQR)2.3921308 × 109

Descriptive statistics

Standard deviation1.4845672 × 109
Coefficient of variation (CV)0.42798416
Kurtosis-0.69460439
Mean3.4687434 × 109
Median Absolute Deviation (MAD)1.0810275 × 109
Skewness0.155261
Sum2.478764 × 1013
Variance2.2039399 × 1018
MonotonicityIncreasing
2024-02-28T21:26:29.614289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3211565100 2
 
< 0.1%
3460532000 2
 
< 0.1%
5290638000 2
 
< 0.1%
4591388000 2
 
< 0.1%
3141167100 2
 
< 0.1%
2550046000 2
 
< 0.1%
1730015000 2
 
< 0.1%
2550103000 2
 
< 0.1%
4590325000 2
 
< 0.1%
4590315000 2
 
< 0.1%
Other values (7045) 7126
99.7%
ValueCountFrequency (%)
30131000 1
< 0.1%
30152000 1
< 0.1%
49980110 1
< 0.1%
49993200 1
< 0.1%
50042000 1
< 0.1%
50074000 1
< 0.1%
50085000 1
< 0.1%
50089000 1
< 0.1%
70017000 1
< 0.1%
70036000 1
< 0.1%
ValueCountFrequency (%)
7160375000 1
< 0.1%
7160366000 1
< 0.1%
7160365000 1
< 0.1%
7160351000 1
< 0.1%
7160339000 1
< 0.1%
7160327000 1
< 0.1%
7160283000 1
< 0.1%
7160279000 1
< 0.1%
7160254000 1
< 0.1%
7160241000 1
< 0.1%
Distinct7055
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size517.2 KiB
2024-02-28T21:26:29.812053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length37
Median length31
Mean length17.094318
Min length12

Characters and Unicode

Total characters122156
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6964 ?
Unique (%)97.5%

Sample

1st row9434-9446 N 107TH ST
2nd row9306-9316 N 107TH ST
3rd row9327 N SWAN RD
4th row9411 W COUNTY LINE RD
5th row9322 N JOYCE AV
ValueCountFrequency (%)
st 4604
 
15.2%
n 3433
 
11.3%
w 1699
 
5.6%
av 1661
 
5.5%
s 1596
 
5.3%
unit 771
 
2.5%
e 431
 
1.4%
pl 285
 
0.9%
dr 180
 
0.6%
rd 122
 
0.4%
Other values (5383) 15604
51.4%
2024-02-28T21:26:30.107515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23240
19.0%
T 9224
 
7.6%
S 7467
 
6.1%
2 5877
 
4.8%
1 5623
 
4.6%
N 5617
 
4.6%
3 5300
 
4.3%
4 4239
 
3.5%
5 4130
 
3.4%
0 4084
 
3.3%
Other values (45) 47355
38.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 53509
43.8%
Decimal Number 41100
33.6%
Space Separator 23240
19.0%
Lowercase Letter 2337
 
1.9%
Dash Punctuation 1196
 
1.0%
Other Punctuation 774
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 9224
17.2%
S 7467
14.0%
N 5617
10.5%
A 3975
 
7.4%
H 3731
 
7.0%
E 3127
 
5.8%
R 2733
 
5.1%
W 2303
 
4.3%
L 2210
 
4.1%
V 1972
 
3.7%
Other values (16) 11150
20.8%
Lowercase Letter
ValueCountFrequency (%)
t 771
33.0%
n 771
33.0%
i 771
33.0%
d 6
 
0.3%
f 4
 
0.2%
j 3
 
0.1%
b 2
 
0.1%
k 2
 
0.1%
a 2
 
0.1%
m 1
 
< 0.1%
Other values (4) 4
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 5877
14.3%
1 5623
13.7%
3 5300
12.9%
4 4239
10.3%
5 4130
10.0%
0 4084
9.9%
6 3307
8.0%
7 3085
7.5%
8 2863
7.0%
9 2592
6.3%
Other Punctuation
ValueCountFrequency (%)
, 771
99.6%
# 2
 
0.3%
\ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
23240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66310
54.3%
Latin 55846
45.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 9224
16.5%
S 7467
13.4%
N 5617
10.1%
A 3975
 
7.1%
H 3731
 
6.7%
E 3127
 
5.6%
R 2733
 
4.9%
W 2303
 
4.1%
L 2210
 
4.0%
V 1972
 
3.5%
Other values (30) 13487
24.2%
Common
ValueCountFrequency (%)
23240
35.0%
2 5877
 
8.9%
1 5623
 
8.5%
3 5300
 
8.0%
4 4239
 
6.4%
5 4130
 
6.2%
0 4084
 
6.2%
6 3307
 
5.0%
7 3085
 
4.7%
8 2863
 
4.3%
Other values (5) 4562
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23240
19.0%
T 9224
 
7.6%
S 7467
 
6.1%
2 5877
 
4.8%
1 5623
 
4.6%
N 5617
 
4.6%
3 5300
 
4.3%
4 4239
 
3.5%
5 4130
 
3.4%
0 4084
 
3.3%
Other values (45) 47355
38.8%

CondoProject
Text

MISSING 

Distinct202
Distinct (%)22.8%
Missing6261
Missing (%)87.6%
Memory size260.0 KiB
2024-02-28T21:26:30.293074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length35
Median length26
Mean length17.267797
Min length5

Characters and Unicode

Total characters15282
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)8.1%

Sample

1st rowNORTHRIDGE WOOD LAKE
2nd rowNORTHRIDGE WOOD LAKE
3rd rowNORTHRIDGE WOOD LAKE
4th rowNORTHRIDGE WOOD LAKE
5th rowNORTHRIDGE WOOD LAKE
ValueCountFrequency (%)
condominium 100
 
4.6%
on 89
 
4.1%
lofts 77
 
3.5%
the 70
 
3.2%
lake 66
 
3.0%
river 58
 
2.7%
condos 55
 
2.5%
condominiums 55
 
2.5%
terrace 39
 
1.8%
point 32
 
1.5%
Other values (250) 1538
70.6%
2024-02-28T21:26:30.593896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1391
 
9.1%
1353
 
8.9%
O 1352
 
8.8%
R 1153
 
7.5%
N 1055
 
6.9%
I 1006
 
6.6%
A 960
 
6.3%
T 796
 
5.2%
S 795
 
5.2%
L 757
 
5.0%
Other values (37) 4664
30.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13616
89.1%
Space Separator 1353
 
8.9%
Decimal Number 128
 
0.8%
Close Punctuation 65
 
0.4%
Open Punctuation 65
 
0.4%
Dash Punctuation 39
 
0.3%
Other Punctuation 9
 
0.1%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1391
 
10.2%
O 1352
 
9.9%
R 1153
 
8.5%
N 1055
 
7.7%
I 1006
 
7.4%
A 960
 
7.1%
T 796
 
5.8%
S 795
 
5.8%
L 757
 
5.6%
D 599
 
4.4%
Other values (16) 3752
27.6%
Decimal Number
ValueCountFrequency (%)
1 37
28.9%
2 30
23.4%
0 20
15.6%
5 18
14.1%
6 18
14.1%
3 2
 
1.6%
4 1
 
0.8%
8 1
 
0.8%
7 1
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
e 2
28.6%
a 2
28.6%
n 1
14.3%
l 1
14.3%
c 1
14.3%
Other Punctuation
ValueCountFrequency (%)
& 4
44.4%
/ 3
33.3%
' 2
22.2%
Space Separator
ValueCountFrequency (%)
1353
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13623
89.1%
Common 1659
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1391
 
10.2%
O 1352
 
9.9%
R 1153
 
8.5%
N 1055
 
7.7%
I 1006
 
7.4%
A 960
 
7.0%
T 796
 
5.8%
S 795
 
5.8%
L 757
 
5.6%
D 599
 
4.4%
Other values (21) 3759
27.6%
Common
ValueCountFrequency (%)
1353
81.6%
) 65
 
3.9%
( 65
 
3.9%
- 39
 
2.4%
1 37
 
2.2%
2 30
 
1.8%
0 20
 
1.2%
5 18
 
1.1%
6 18
 
1.1%
& 4
 
0.2%
Other values (6) 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1391
 
9.1%
1353
 
8.9%
O 1352
 
8.8%
R 1153
 
7.5%
N 1055
 
6.9%
I 1006
 
6.6%
A 960
 
6.3%
T 796
 
5.2%
S 795
 
5.2%
L 757
 
5.0%
Other values (37) 4664
30.5%

District
Real number (ℝ)

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8376714
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:30.694879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile14
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2621201
Coefficient of variation (CV)0.54379929
Kurtosis-1.2297748
Mean7.8376714
Median Absolute Deviation (MAD)3
Skewness0.01487817
Sum56008
Variance18.165668
MonotonicityNot monotonic
2024-02-28T21:26:30.779630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 778
10.9%
11 612
 
8.6%
10 586
 
8.2%
14 562
 
7.9%
2 531
 
7.4%
7 530
 
7.4%
13 524
 
7.3%
3 513
 
7.2%
9 488
 
6.8%
1 468
 
6.5%
Other values (5) 1554
21.7%
ValueCountFrequency (%)
1 468
6.5%
2 531
7.4%
3 513
7.2%
4 322
4.5%
5 778
10.9%
6 366
5.1%
7 530
7.4%
8 273
 
3.8%
9 488
6.8%
10 586
8.2%
ValueCountFrequency (%)
15 297
4.2%
14 562
7.9%
13 524
7.3%
12 296
4.1%
11 612
8.6%
10 586
8.2%
9 488
6.8%
8 273
3.8%
7 530
7.4%
6 366
5.1%

nbhd
Real number (ℝ)

Distinct459
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3338.4563
Minimum40
Maximum24910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:30.890736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile780
Q11780
median3060
Q34620
95-th percentile6277
Maximum24910
Range24870
Interquartile range (IQR)2840

Descriptive statistics

Standard deviation1795.1757
Coefficient of variation (CV)0.53772626
Kurtosis1.7286574
Mean3338.4563
Median Absolute Deviation (MAD)1520
Skewness0.36972966
Sum23856609
Variance3222655.6
MonotonicityNot monotonic
2024-02-28T21:26:31.005354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 156
 
2.2%
2080 132
 
1.8%
4520 124
 
1.7%
4120 119
 
1.7%
1140 118
 
1.7%
4420 101
 
1.4%
4240 99
 
1.4%
4340 98
 
1.4%
4620 93
 
1.3%
1440 92
 
1.3%
Other values (449) 6014
84.2%
ValueCountFrequency (%)
40 14
 
0.2%
50 4
 
0.1%
240 61
0.9%
360 31
0.4%
380 14
 
0.2%
440 43
0.6%
480 71
1.0%
520 8
 
0.1%
560 41
0.6%
600 22
 
0.3%
ValueCountFrequency (%)
24910 1
 
< 0.1%
6982 1
 
< 0.1%
6981 1
 
< 0.1%
6980 1
 
< 0.1%
6979 1
 
< 0.1%
6978 1
 
< 0.1%
6977 2
< 0.1%
6976 1
 
< 0.1%
6974 4
0.1%
6973 1
 
< 0.1%

Style
Text

Distinct81
Distinct (%)1.1%
Missing21
Missing (%)0.3%
Memory size484.5 KiB
2024-02-28T21:26:31.142948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length50
Median length48
Mean length12.516491
Min length5

Characters and Unicode

Total characters89180
Distinct characters63
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st rowService Building
2nd rowOffice Building - 1 Story
3rd rowRanch
4th rowRanch
5th rowRanch
ValueCountFrequency (%)
ranch 1507
 
8.8%
o/s 1174
 
6.8%
cod 1006
 
5.9%
cape 1006
 
5.9%
999
 
5.8%
duplex 954
 
5.6%
bungalow 871
 
5.1%
rise 681
 
4.0%
stories 681
 
4.0%
res 628
 
3.7%
Other values (146) 7656
44.6%
2024-02-28T21:26:31.440283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10040
 
11.3%
e 6566
 
7.4%
o 5544
 
6.2%
a 4859
 
5.4%
n 4344
 
4.9%
l 4294
 
4.8%
i 3979
 
4.5%
t 3052
 
3.4%
C 3005
 
3.4%
p 2981
 
3.3%
Other values (53) 40516
45.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54926
61.6%
Uppercase Letter 16158
 
18.1%
Space Separator 10040
 
11.3%
Decimal Number 3547
 
4.0%
Other Punctuation 2687
 
3.0%
Dash Punctuation 1017
 
1.1%
Open Punctuation 303
 
0.3%
Math Symbol 260
 
0.3%
Close Punctuation 242
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6566
12.0%
o 5544
10.1%
a 4859
 
8.8%
n 4344
 
7.9%
l 4294
 
7.8%
i 3979
 
7.2%
t 3052
 
5.6%
p 2981
 
5.4%
u 2972
 
5.4%
s 2913
 
5.3%
Other values (15) 13422
24.4%
Uppercase Letter
ValueCountFrequency (%)
C 3005
18.6%
R 2926
18.1%
S 2678
16.6%
D 1450
9.0%
O 1283
7.9%
B 1115
 
6.9%
A 993
 
6.1%
M 988
 
6.1%
T 387
 
2.4%
N 384
 
2.4%
Other values (10) 949
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 1449
40.9%
2 1114
31.4%
4 509
 
14.4%
3 257
 
7.2%
6 161
 
4.5%
7 41
 
1.2%
0 13
 
0.4%
8 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 2002
74.5%
& 589
 
21.9%
, 88
 
3.3%
. 8
 
0.3%
Math Symbol
ValueCountFrequency (%)
+ 162
62.3%
> 98
37.7%
Space Separator
ValueCountFrequency (%)
10040
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1017
100.0%
Open Punctuation
ValueCountFrequency (%)
( 303
100.0%
Close Punctuation
ValueCountFrequency (%)
) 242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71084
79.7%
Common 18096
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6566
 
9.2%
o 5544
 
7.8%
a 4859
 
6.8%
n 4344
 
6.1%
l 4294
 
6.0%
i 3979
 
5.6%
t 3052
 
4.3%
C 3005
 
4.2%
p 2981
 
4.2%
u 2972
 
4.2%
Other values (35) 29488
41.5%
Common
ValueCountFrequency (%)
10040
55.5%
/ 2002
 
11.1%
1 1449
 
8.0%
2 1114
 
6.2%
- 1017
 
5.6%
& 589
 
3.3%
4 509
 
2.8%
( 303
 
1.7%
3 257
 
1.4%
) 242
 
1.3%
Other values (8) 574
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10040
 
11.3%
e 6566
 
7.4%
o 5544
 
6.2%
a 4859
 
5.4%
n 4344
 
4.9%
l 4294
 
4.8%
i 3979
 
4.5%
t 3052
 
3.4%
C 3005
 
3.4%
p 2981
 
3.3%
Other values (53) 40516
45.4%

Extwall
Categorical

MISSING 

Distinct18
Distinct (%)0.3%
Missing926
Missing (%)13.0%
Memory size471.1 KiB
Aluminum/Vinyl
3468 
Brick
1408 
Wood
 
331
Asphalt/Other
 
313
Stone
 
179
Other values (13)
521 

Length

Max length23
Median length14
Mean length11.009646
Min length4

Characters and Unicode

Total characters68480
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowConcrete Block
2nd rowBrick
3rd rowAluminum/Vinyl
4th rowAluminum/Vinyl
5th rowAluminum/Vinyl

Common Values

ValueCountFrequency (%)
Aluminum/Vinyl 3468
48.5%
Brick 1408
19.7%
Wood 331
 
4.6%
Asphalt/Other 313
 
4.4%
Stone 179
 
2.5%
Masonry/Frame 154
 
2.2%
Stucco 93
 
1.3%
Concrete Block 77
 
1.1%
Fiber Cement/Hardiplank 49
 
0.7%
Alum/Vynyl Siding 46
 
0.6%
Other values (8) 102
 
1.4%
(Missing) 926
 
13.0%

Length

2024-02-28T21:26:31.573183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aluminum/vinyl 3468
53.7%
brick 1410
21.8%
wood 342
 
5.3%
asphalt/other 313
 
4.8%
stone 179
 
2.8%
masonry/frame 154
 
2.4%
block 104
 
1.6%
stucco 93
 
1.4%
concrete 77
 
1.2%
siding 58
 
0.9%
Other values (10) 262
 
4.1%

Most occurring characters

ValueCountFrequency (%)
i 8560
12.5%
n 7591
11.1%
l 7506
11.0%
m 7224
10.5%
u 7075
10.3%
/ 4030
 
5.9%
A 3827
 
5.6%
y 3755
 
5.5%
V 3514
 
5.1%
r 2310
 
3.4%
Other values (22) 13088
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53722
78.4%
Uppercase Letter 10488
 
15.3%
Other Punctuation 4030
 
5.9%
Space Separator 240
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 8560
15.9%
n 7591
14.1%
l 7506
14.0%
m 7224
13.4%
u 7075
13.2%
y 3755
7.0%
r 2310
 
4.3%
c 1791
 
3.3%
k 1563
 
2.9%
o 1334
 
2.5%
Other values (9) 5013
9.3%
Uppercase Letter
ValueCountFrequency (%)
A 3827
36.5%
V 3514
33.5%
B 1514
 
14.4%
W 342
 
3.3%
S 330
 
3.1%
O 323
 
3.1%
F 231
 
2.2%
M 207
 
2.0%
C 126
 
1.2%
H 49
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/ 4030
100.0%
Space Separator
ValueCountFrequency (%)
240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64210
93.8%
Common 4270
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 8560
13.3%
n 7591
11.8%
l 7506
11.7%
m 7224
11.3%
u 7075
11.0%
A 3827
 
6.0%
y 3755
 
5.8%
V 3514
 
5.5%
r 2310
 
3.6%
c 1791
 
2.8%
Other values (20) 11057
17.2%
Common
ValueCountFrequency (%)
/ 4030
94.4%
240
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 8560
12.5%
n 7591
11.1%
l 7506
11.0%
m 7224
10.5%
u 7075
10.3%
/ 4030
 
5.9%
A 3827
 
5.6%
y 3755
 
5.5%
V 3514
 
5.1%
r 2310
 
3.4%
Other values (22) 13088
19.1%

Stories
Real number (ℝ)

Distinct13
Distinct (%)0.2%
Missing39
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.3848319
Minimum0
Maximum14
Zeros21
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:31.663041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5348119
Coefficient of variation (CV)0.38619266
Kurtosis69.638741
Mean1.3848319
Median Absolute Deviation (MAD)0
Skewness4.044354
Sum9842
Variance0.28602376
MonotonicityNot monotonic
2024-02-28T21:26:31.762630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 3955
55.3%
2 2008
28.1%
1.5 1015
 
14.2%
3 52
 
0.7%
2.5 40
 
0.6%
0 21
 
0.3%
4 7
 
0.1%
5 3
 
< 0.1%
7 2
 
< 0.1%
12 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 39
 
0.5%
ValueCountFrequency (%)
0 21
 
0.3%
1 3955
55.3%
1.5 1015
 
14.2%
2 2008
28.1%
2.5 40
 
0.6%
3 52
 
0.7%
3.5 1
 
< 0.1%
4 7
 
0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 1
 
< 0.1%
7 2
 
< 0.1%
6 1
 
< 0.1%
5 3
 
< 0.1%
4 7
 
0.1%
3.5 1
 
< 0.1%
3 52
 
0.7%
2.5 40
 
0.6%
2 2008
28.1%

Year_Built
Real number (ℝ)

Distinct155
Distinct (%)2.2%
Missing11
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1936.1706
Minimum0
Maximum2022
Zeros20
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:31.868253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1892
Q11921
median1948
Q31958
95-th percentile1999
Maximum2022
Range2022
Interquartile range (IQR)37

Descriptive statistics

Standard deviation106.7051
Coefficient of variation (CV)0.055111416
Kurtosis301.17114
Mean1936.1706
Median Absolute Deviation (MAD)20
Skewness-16.740934
Sum13814577
Variance11385.979
MonotonicityNot monotonic
2024-02-28T21:26:31.987912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1955 249
 
3.5%
1952 212
 
3.0%
1951 196
 
2.7%
1954 195
 
2.7%
1953 193
 
2.7%
1956 183
 
2.6%
1950 178
 
2.5%
1957 177
 
2.5%
1958 168
 
2.4%
1926 138
 
1.9%
Other values (145) 5246
73.4%
ValueCountFrequency (%)
0 20
0.3%
1836 1
 
< 0.1%
1843 1
 
< 0.1%
1855 2
 
< 0.1%
1860 2
 
< 0.1%
1861 2
 
< 0.1%
1865 3
 
< 0.1%
1868 1
 
< 0.1%
1869 1
 
< 0.1%
1870 15
0.2%
ValueCountFrequency (%)
2022 6
0.1%
2020 1
 
< 0.1%
2019 1
 
< 0.1%
2018 3
< 0.1%
2017 2
 
< 0.1%
2016 4
0.1%
2015 1
 
< 0.1%
2014 2
 
< 0.1%
2013 3
< 0.1%
2012 1
 
< 0.1%

Rooms
Real number (ℝ)

MISSING  ZEROS 

Distinct40
Distinct (%)0.6%
Missing443
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean7.7193794
Minimum0
Maximum63
Zeros122
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:32.105704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median7
Q310
95-th percentile14.9
Maximum63
Range63
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1556761
Coefficient of variation (CV)0.53834329
Kurtosis15.325323
Mean7.7193794
Median Absolute Deviation (MAD)2
Skewness2.4306361
Sum51743
Variance17.269644
MonotonicityNot monotonic
2024-02-28T21:26:32.218971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
5 1427
20.0%
6 991
13.9%
10 905
12.7%
4 644
9.0%
8 573
8.0%
7 512
 
7.2%
12 410
 
5.7%
9 305
 
4.3%
14 141
 
2.0%
11 137
 
1.9%
Other values (30) 658
9.2%
(Missing) 443
 
6.2%
ValueCountFrequency (%)
0 122
 
1.7%
1 6
 
0.1%
2 21
 
0.3%
3 128
 
1.8%
4 644
9.0%
5 1427
20.0%
6 991
13.9%
7 512
 
7.2%
8 573
8.0%
9 305
 
4.3%
ValueCountFrequency (%)
63 1
 
< 0.1%
62 1
 
< 0.1%
45 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 2
 
< 0.1%
30 8
0.1%

FinishedSqft
Real number (ℝ)

Distinct2386
Distinct (%)33.5%
Missing24
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2334.2627
Minimum0
Maximum245266
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:32.331938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile787
Q11082
median1402.5
Q32014
95-th percentile3672
Maximum245266
Range245266
Interquartile range (IQR)932

Descriptive statistics

Standard deviation8425.9847
Coefficient of variation (CV)3.6096986
Kurtosis406.49186
Mean2334.2627
Median Absolute Deviation (MAD)397.5
Skewness18.519566
Sum16624619
Variance70997219
MonotonicityNot monotonic
2024-02-28T21:26:32.709961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1056 30
 
0.4%
936 30
 
0.4%
864 29
 
0.4%
1120 26
 
0.4%
672 26
 
0.4%
1054 25
 
0.3%
1140 23
 
0.3%
2040 22
 
0.3%
980 21
 
0.3%
912 18
 
0.3%
Other values (2376) 6872
96.2%
(Missing) 24
 
0.3%
ValueCountFrequency (%)
0 6
0.1%
325 4
0.1%
405 2
 
< 0.1%
430 2
 
< 0.1%
460 3
< 0.1%
476 1
 
< 0.1%
496 1
 
< 0.1%
498 2
 
< 0.1%
500 1
 
< 0.1%
508 1
 
< 0.1%
ValueCountFrequency (%)
245266 1
< 0.1%
232960 1
< 0.1%
210744 1
< 0.1%
202568 1
< 0.1%
196753 1
< 0.1%
170090 1
< 0.1%
156025 1
< 0.1%
141787 1
< 0.1%
139280 1
< 0.1%
127812 1
< 0.1%

Units
Real number (ℝ)

SKEWED 

Distinct49
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0100756
Minimum0
Maximum737
Zeros29
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:32.814749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum737
Range737
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.166496
Coefficient of variation (CV)7.047743
Kurtosis2070.7177
Mean2.0100756
Median Absolute Deviation (MAD)0
Skewness42.806223
Sum14364
Variance200.68961
MonotonicityNot monotonic
2024-02-28T21:26:32.926819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 5070
70.9%
2 1564
 
21.9%
4 166
 
2.3%
3 135
 
1.9%
8 40
 
0.6%
0 29
 
0.4%
6 28
 
0.4%
5 25
 
0.3%
7 16
 
0.2%
12 8
 
0.1%
Other values (39) 65
 
0.9%
ValueCountFrequency (%)
0 29
 
0.4%
1 5070
70.9%
2 1564
 
21.9%
3 135
 
1.9%
4 166
 
2.3%
5 25
 
0.3%
6 28
 
0.4%
7 16
 
0.2%
8 40
 
0.6%
9 5
 
0.1%
ValueCountFrequency (%)
737 1
< 0.1%
725 1
< 0.1%
389 1
< 0.1%
300 1
< 0.1%
116 1
< 0.1%
115 1
< 0.1%
101 1
< 0.1%
99 1
< 0.1%
94 2
< 0.1%
84 1
< 0.1%

Bdrms
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)0.4%
Missing443
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean3.9258541
Minimum0
Maximum32
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:33.027251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q35
95-th percentile8
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0797352
Coefficient of variation (CV)0.52975355
Kurtosis17.125512
Mean3.9258541
Median Absolute Deviation (MAD)1
Skewness2.6006406
Sum26315
Variance4.3252983
MonotonicityNot monotonic
2024-02-28T21:26:33.113144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 2200
30.8%
4 1484
20.8%
2 1037
14.5%
6 869
 
12.2%
5 371
 
5.2%
1 242
 
3.4%
8 215
 
3.0%
7 90
 
1.3%
10 53
 
0.7%
12 44
 
0.6%
Other values (14) 98
 
1.4%
(Missing) 443
 
6.2%
ValueCountFrequency (%)
0 18
 
0.3%
1 242
 
3.4%
2 1037
14.5%
3 2200
30.8%
4 1484
20.8%
5 371
 
5.2%
6 869
 
12.2%
7 90
 
1.3%
8 215
 
3.0%
9 38
 
0.5%
ValueCountFrequency (%)
32 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
25 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
18 4
 
0.1%
16 1
 
< 0.1%
15 4
 
0.1%
14 10
0.1%

Fbath
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4354884
Minimum0
Maximum7
Zeros509
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:33.189272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.71871514
Coefficient of variation (CV)0.50067639
Kurtosis1.2512878
Mean1.4354884
Median Absolute Deviation (MAD)1
Skewness0.36124013
Sum10258
Variance0.51655145
MonotonicityNot monotonic
2024-02-28T21:26:33.272090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3409
47.7%
2 2885
40.4%
0 509
 
7.1%
3 304
 
4.3%
4 31
 
0.4%
5 6
 
0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 509
 
7.1%
1 3409
47.7%
2 2885
40.4%
3 304
 
4.3%
4 31
 
0.4%
5 6
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 1
 
< 0.1%
5 6
 
0.1%
4 31
 
0.4%
3 304
 
4.3%
2 2885
40.4%
1 3409
47.7%
0 509
 
7.1%

Hbath
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size404.9 KiB
0
5183 
1
1793 
2
 
164
3
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7146
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Length

2024-02-28T21:26:33.367635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T21:26:33.447451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7146
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7146
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5183
72.5%
1 1793
 
25.1%
2 164
 
2.3%
3 6
 
0.1%

Lotsize
Real number (ℝ)

SKEWED  ZEROS 

Distinct1670
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6676.4801
Minimum0
Maximum1341648
Zeros489
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:33.542986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13660
median5002
Q36750
95-th percentile11314.75
Maximum1341648
Range1341648
Interquartile range (IQR)3090

Descriptive statistics

Standard deviation24988.764
Coefficient of variation (CV)3.7428051
Kurtosis1483.0128
Mean6676.4801
Median Absolute Deviation (MAD)1402
Skewness33.886178
Sum47710127
Variance6.2443833 × 108
MonotonicityNot monotonic
2024-02-28T21:26:33.674840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 489
 
6.8%
1 456
 
6.4%
4800 419
 
5.9%
3600 341
 
4.8%
6000 171
 
2.4%
5400 136
 
1.9%
7200 135
 
1.9%
5000 117
 
1.6%
4920 95
 
1.3%
4200 92
 
1.3%
Other values (1660) 4695
65.7%
ValueCountFrequency (%)
0 489
6.8%
1 456
6.4%
75 1
 
< 0.1%
613 1
 
< 0.1%
929 1
 
< 0.1%
1050 1
 
< 0.1%
1080 1
 
< 0.1%
1084 1
 
< 0.1%
1098 1
 
< 0.1%
1120 1
 
< 0.1%
ValueCountFrequency (%)
1341648 1
< 0.1%
835916 1
< 0.1%
788775 1
< 0.1%
429109 1
< 0.1%
409333 1
< 0.1%
388990 1
< 0.1%
306096 1
< 0.1%
277825 1
< 0.1%
261360 1
< 0.1%
243848 1
< 0.1%
Distinct313
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size56.0 KiB
Minimum2022-01-01 00:00:00
Maximum2022-12-30 00:00:00
2024-02-28T21:26:33.798097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:33.933187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sale_price
Real number (ℝ)

Distinct1284
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271544.97
Minimum4000
Maximum21850000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.0 KiB
2024-02-28T21:26:34.066665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile64000
Q1131000
median195000
Q3260000
95-th percentile481720
Maximum21850000
Range21846000
Interquartile range (IQR)129000

Descriptive statistics

Standard deviation770141.28
Coefficient of variation (CV)2.8361464
Kurtosis399.43511
Mean271544.97
Median Absolute Deviation (MAD)65000
Skewness18.427415
Sum1.9404603 × 109
Variance5.931176 × 1011
MonotonicityNot monotonic
2024-02-28T21:26:34.182267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 117
 
1.6%
250000 117
 
1.6%
220000 104
 
1.5%
160000 101
 
1.4%
225000 99
 
1.4%
150000 95
 
1.3%
180000 94
 
1.3%
190000 93
 
1.3%
175000 91
 
1.3%
210000 90
 
1.3%
Other values (1274) 6145
86.0%
ValueCountFrequency (%)
4000 1
< 0.1%
5000 1
< 0.1%
7000 1
< 0.1%
9000 1
< 0.1%
10000 2
< 0.1%
11000 1
< 0.1%
12500 1
< 0.1%
15000 2
< 0.1%
16000 1
< 0.1%
18000 1
< 0.1%
ValueCountFrequency (%)
21850000 1
< 0.1%
20828000 1
< 0.1%
20000000 1
< 0.1%
17400000 1
< 0.1%
17225000 1
< 0.1%
14600000 2
< 0.1%
14500000 1
< 0.1%
14450000 1
< 0.1%
14250000 1
< 0.1%
13735000 1
< 0.1%

Interactions

2024-02-28T21:26:27.433510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:13.718449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.774580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.857856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.880080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.928349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.072779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.231947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.783118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.816326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.911405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.963451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.247161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.515029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:13.796514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.857646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.941199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.960095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.019985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.157739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.317798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.859854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.897102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.998328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.045236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.334095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.598350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:13.877002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.942561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.020270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.039946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.105850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.243122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.400218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.938635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.977393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.088168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.127609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.424726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.678817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:13.969074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.034460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.093703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.114900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.185270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.326484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.483527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.022921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.066410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.168118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.209187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.509142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.752855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.048588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.112212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.171214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.195731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.281804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.424989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.568612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.102539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.146275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.244068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.312678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.603103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.834768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.129902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.193581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.249065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.277814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.375023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.512140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.647613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.180726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.231183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.325910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.396808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.723011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.916313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.217640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.278715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.334009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.364369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.463790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.593361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.236310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.262150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.330066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.409882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.483282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.818300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.998851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.297500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.360394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.410976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.440646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.547982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.676499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.318367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.340442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.414530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.486104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.559620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.897572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:28.077651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.373874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.439536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.486704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.522329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.640826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.755335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.398198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.416240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.492011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.564428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.636792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.980138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:28.160156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.455765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.521976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.563960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.601766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.745277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.842438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.475995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.495563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.577471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.647315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.716657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.076797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:28.237844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.532130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.615908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.639543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.678997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.826808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:19.946615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.552241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.573536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.657740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.727190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:25.999438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.179781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:28.319635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.612503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.701015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.719524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.756355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.911204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.030967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.634581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.653295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.737886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.809965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.081371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.264294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:28.403326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:14.695851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:15.786670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:16.802758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:17.840232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:18.996646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:20.133955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:21.709282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:22.743949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:23.831562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:24.883347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:26.168037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:26:27.350877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-28T21:26:28.525409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-28T21:26:28.748133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PropertyIDPropTypetaxkeyAddressCondoProjectDistrictnbhdStyleExtwallStoriesYear_BuiltRoomsFinishedSqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_price
098461Manufacturing301310009434-9446 N 107TH STNaN96300Service BuildingConcrete Block1.01978.0NaN20600.06NaN0002022-04-01950000.0
198464Commercial301520009306-9316 N 107TH STNaN96202Office Building - 1 StoryBrick1.01982.0NaN9688.023NaN00357192022-10-07385000.0
298508Residential499801109327 N SWAN RDNaN940NaNNaNNaNNaNNaNNaN0NaN0013416482022-01-07800000.0
398519Residential499932009411 W COUNTY LINE RDNaN940RanchAluminum/Vinyl1.01959.06.01334.013.011832002022-08-09280000.0
498561Residential500420009322 N JOYCE AVNaN940RanchAluminum/Vinyl1.01980.010.01006.016.01083032022-05-23233100.0
598593Residential500740009360 N 85TH STNaN940RanchAluminum/Vinyl1.01982.05.01007.013.01072002022-07-25215000.0
698604Residential500850009305 N BURBANK AVNaN940RanchAluminum/Vinyl1.01984.05.01301.013.02072002022-03-29150000.0
798608Residential500890009217 N 83RD STNaN940ColonialAluminum/Vinyl2.02007.09.02237.014.021156772022-05-10400000.0
898696Condominium700170009192 N 70TH ST, Unit 2NORTHRIDGE WOOD LAKE95010Condo TownhouseNaN2.01973.07.01437.013.02102022-05-16122000.0
998715Condominium700360009212 N 70TH ST, Unit 8NORTHRIDGE WOOD LAKE95010Condo TownhouseNaN2.01973.07.01437.014.02102022-04-14123000.0
PropertyIDPropTypetaxkeyAddressCondoProjectDistrictnbhdStyleExtwallStoriesYear_BuiltRoomsFinishedSqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_price
7136260546Residential71602410001821 W SALEM STNaN134920RanchAluminum/Vinyl1.01960.05.0965.013.01060002022-07-22220000.0
7137260559Residential71602540006507 S 17TH STNaN134920RanchAluminum/Vinyl1.01960.05.01060.013.01160482022-08-22240000.0
7138260584Residential71602790006444 S 18TH STNaN134920RanchAluminum/Vinyl1.01961.05.0982.013.01070002022-08-30195000.0
7139260588Residential71602830006465 S 18TH STNaN134920RanchAluminum/Vinyl1.01960.010.0965.016.01060932022-10-12260000.0
7140260630Condominium71603270001928 W SALEM STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN1.01974.05.01141.012.02012022-11-21159900.0
7141260642Condominium71603390001912 W SALEM STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN2.01974.010.01100.014.01112022-03-11125900.0
7142260654Condominium71603510006316 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN1.01974.05.01379.012.01112022-10-28150000.0
7143260668Condominium71603650006376 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN2.01974.010.01100.014.01112022-03-15130000.0
7144260669Condominium71603660006378 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN2.01974.05.01100.012.01112022-12-30123000.0
7145260678Condominium71603750006354 S 20TH STCOLLEGE HEIGHTS135360Low Rise 1-3 StoriesNaN1.01974.05.01141.012.01112022-07-08157500.0